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# tf.contrib.metrics.streaming_sparse_average_precision_at_top_k

Computes average precision@k of predictions with respect to sparse labels.

streaming_sparse_average_precision_at_top_k creates two local variables, average_precision_at_<k>/total and average_precision_at_<k>/max, that are used to compute the frequency. This frequency is ultimately returned as average_precision_at_<k>: an idempotent operation that simply divides average_precision_at_<k>/total by average_precision_at_<k>/max.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the precision_at_<k>. Set operations applied to top_k and labels calculate the true positives and false positives weighted by weights. Then update_op increments true_positive_at_<k> and false_positive_at_<k> using these values.

If weights is None, weights default to 1. Use weights of 0 to mask values.

top_k_predictions Integer Tensor with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and predictions_idx has shape [batch size, k]. The final dimension must be set and contains the top k predicted class indices. [D1, ... DN] must match labels. Values should be in range [0, num_classes).
labels int64 Tensor or SparseTensor with shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and labels has shape [batch_size, num_labels]. [D1, ... DN] must match top_k_predictions. Values should be in range [0, num_classes).
weights Tensor whose rank is either 0, or n-1, where n is the rank of labels. If the latter, it must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding labels dimension).
metrics_collections An optional list of collections that values should be added to.